Scanning and Mining of High Fecundity Genes by Oxford Nanopore Technologies (ONT) in Sheep (Ovis aries) Pituitary

Scanning and Mining of High Fecundity Genes by Oxford Nanopore Technologies (ONT) in Sheep (Ovis aries) Pituitary | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Scanning and Mining of High Fecundity Genes by Oxford Nanopore Technologies (ONT) in Sheep (Ovis aries) Pituitary Xue Xiao, Lin Ju, Zhibin Ji, Tong Wang, Dejie Zhu, Zhonghui Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4812389/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2025 Read the published version in BMC Genomics → Version 1 posted 4 You are reading this latest preprint version Abstract Background Reproduction is a complex process, which is influenced by the inheritance of many minor genes and some major genes. The pituitary gland is an important endocrine organ that regulates estrus and reproduction in sheep mainly through hormone synthesis and secretion. Previous studies on reproduction traits have focused mainly on folliculogenesis and ovulation in sheep with different fecundities, and few systematic analyses of the mRNAs expressed in the pituitary have been performed. To explore the intrinsic molecular regulatory mechanisms and gene regulatory network of sheep reproductive traits, key genes affecting multiple fetal traits, such as ovulation number and litter size, were screened to provide a new reference for the study of reproduction traits in sheep. Result In this study, three healthy small-tailed Han sheep and three healthy Wadi sheep were selected to form a high-reproduction group (small-tailed Han sheep, HP group) and a low-reproduction group (Wadi sheep, LP group). ONT full-length transcriptome sequencing technology was used for mRNA identification, screening, and functional analysis. A total of 7,123 DEGs were found between the two groups of sheep, including 3,551 genes that were upregulated and 3,572 genes that were downregulated in the HP group. The expression of screened genes PRKACB , MAPK1 , CAMK2D , PIK3CB , GNAI3 , RAC1 , PTK2 , ITGB1 , PRKCB , MAPK10 , and MAPK13 significantly differed between the HP and LP groups. GO and KEGG terms related to pituitary function and reproduction were enriched, including reproductive processes, responses to stimuli, and synapses. The related pathways included the mTOR signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, ERK1/2 signaling pathways and MAPK signaling pathways. Conclusions Our results clearly indicate that the DEGs detected were involved in the structure development of tissues and organs, as well as the secretion of hormones in the endocrine system, which could provide a scientific basis for elucidating the genetic mechanisms of high reproduction in sheep. sheep pituitary fecundity ONT sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Among grass-fed livestock, sheep have a high feed conversion rate, a wide range of food intakes and adaptabilities, and the ability to make full use of resources that other livestock cannot. In recent years, with social and economic development, the consumption of mutton has increased, so new breeds of sheep with fast growth, high fecundity, and good carcass quality is urgently needed [ 1 , 2 ]. Small-tailed Han sheep have excellent reproductive traits, such as precocious maturity, perennial estrus, and multiple lambs, making them one of the world-renowned high-reproduction sheep breeds. Wadi sheep are local breeds in Shandong Province that mature early but have a relatively low reproductive rate. Oxford Nanopore Technologies (ONT) is an emerging third-generation single-molecule sequencing technology. It is a platform for MinION sequencing [ 3 ] that can be used to comprehensively and rapidly obtain information on virtually all transcripts of a particular organ or tissue of a species in a particular state, and has seen wide usage in genomics research. Reproduction is a complex process. Traits such as ovulation rate and litter size are influenced by the inheritance of many minor genes and some major genes. Genes associated with litter size, such as bone morphogenetic protein receptor IB (BMPRIB), bone morphogenetic protein 15 (BMP15), and growth differentiation factor 9 (GDF9), are located on sheep chromosomes 6, X, and 5 [ 4 ]. Reproductive traits are important economic traits in sheep farming and are affected by many factors, but the molecular regulatory mechanisms in pituitary tissue need to be further studied. The pituitary gland is an important endocrine organ that regulates estrus and reproduction in sheep mainly through hormone synthesis and secretion [ 5 ]. Previous studies on reproduction traits have focused mainly on folliculogenesis and ovulation in sheep with different fecundities, and few systematic analyses of the mRNAs expressed in the pituitary have been performed. Therefore, in this study, sequencing analysis of the pituitary glands of Wadi sheep and small-tailed Han sheep of different fecundities was performed. The findings enriche the genetic information in the pituitary gland of sheep and provides candidate molecules at the gene level that may regulate multiple fetuses in sheep. We hope that this study will provide a theoretical basis for analyzing sheep reproductive traits and improving sheep production performance. Results RNA Quality Detection and Sequencing Quality Analysis The ONT method was used to sequence the pituitary tissues of small-tailed Han sheep (high reproduction) and Wadi sheep (low reproduction), and three biological samples were run in each group. The obtained data were shown in Table 1. The original data of the high reproduction group (X1, X2, X3) and the low reproduction group (W1, W2, W3) included 8,606,523, 9,885,400, 8,981,989, 5,647,518, 5,273,049, and 5,207,590 reads, respectively. After removing reads with adapters and low quality, there were 8,167,307, 9,581,802, 8,630,100, 5,444,801, 5,030,988, and 4,925,563 clean reads, respectively. And the proportion of clean reads after filtering was more than 90%. The length of N50 of the six libraries obtained from sequencing were 917, 732, 866, 1,054, 1,039, and 1,244 bp, respectively. As can be seen from the Table 1, the length of N50 in the high reproduction group clearly differed from that of N50 in the low reproduction group, which could be used in the functional analysis of the differentially expressed genes (DEGs). The alignment rates of the identified full-length sequences to the reference genome were 84.33%, 84.22%, 86.98%, 97.83%, 97.83%, 98.7%, and 97.86%, respectively. These results indicated that the six constructed libraries and the data were of good quality for use in further analysis. Identification and Expression Analysis of DEGs The level of gene expression is regulated by various factors. The amount of transcribed mRNA varies between tissues and growth stages. In this study, a box plot was drawn for each sample. The overall expression trend of the three pituitary samples in the high reproduction group was the same, and the distribution trend of the overall expression levels of the three pituitary samples in the low reproduction group was consistent. The overall gene expression levels of the high reproduction group were significantly lower than those in the low reproduction group (Fig. 1 A and Fig. 1 B, Table S1 ). Comparing the expression levels of all the genes between the high and low reproduction groups (Fig. 1 C), there were 7,123 DEGs between the two groups, 3,551 being upregulated and 3,572 downregulated in the high reproduction group. To more intuitively understand the overall distribution of the DEGs, we used the log 2 (fold change), basemean, and p adj (the p value after multiple-hypothesis testing correction) to construct the MA plot and volcano plot (Fig. 1 D and 1 E, Table S1 ). From those we can clearly see that the genes obtained by sequencing show a clear differential distribution of up- and down-regulation among the different groups. The cluster analysis based on the DEGs (Fig. 1 F) showed that the individuals in the high reproduction group (X1, X2, X3) clustered together and the individuals in the low reproduction group (W1, W2, W3) clustered together. The expression patterns of the DEGs between two groups were significantly different. We speculated that the DEGs found here were involved in the regulation of reproductive performance in sheep. GO Enrichment Analysis of DEGs For the 7,123 DEGs obtained from sequencing, GO annotation was performed for the three categories of molecular function, cellular components, and biological processes. Among them, 4,425 genes were annotated in the GO database, and a total of 5,930 terms were enriched, of which 3,654 (62%) were classified under biological processes, 789 (13%) were classified under cellular components, and 1,496 (25%) were classified under molecular functions. In the two groups, the most significantly enriched biological processes were regulation of transcription and DNA templated, followed by signal transduction and translation; the most significantly enriched molecular functions were metal ion binding, ATP binding, and calcium ion binding. The enrichment of cellular components was mainly concentrated in the cytoplasm, nucleus, plasma membrane, and integral component of membrane. The number and significant expression status of genes enriched for each enrichment entry are shown (Fig. 2 , Table S2 ). Based on the above results, we speculated that the DEGs between the two groups play important biological roles in different parts of the cell through transcriptional regulation, signal transduction, catalytic binding, and other pathways. GO Function Annotation of the DEGs in the High Reproduction Group By annotating 1,622 upregulated genes in the high reproduction group into the GO database, a total of 2,866 terms were enriched. Among them, 1,711 terms were enriched for biological processes, 441 terms were enriched for cellular components, and 714 terms were enriched for molecular functions. Significant enrichment in biological processes included intracellular signal transduction, regulation of ion transmembrane transport, and nervous system development. The main enriched cellular components included the membrane and cytoskeleton. The main enriched molecular functions included ATP binding, calciumion binding, and protein kinase activity (Fig. 3 A and 3 B, Table S3 ). By annotating 2,803 downregulated genes in the high reproduction group to the GO database, a total of 4,716 terms were enriched, including 2,878 biological process, 650 cellular component, and 1,188 molecular function. The biological processes mainly included translation and intracellular protein transport; the cellular components mainly included the cytoplasm, cytosol, and endoplasmic reticulum membrane; and the molecular functions mainly included structural constituents of ribosomes, GTP binding, identical protein binding, and GTPase activity (Fig. 3 C and 3 D, Table S3 ). We speculated that the DEGs may regulate the physiological processes of sheep reproduction through biological processes such as cell signal transduction, ATP binding, and protein transport. KEGG Enrichment Analysis of DEGs To further identify the main functions of the DEGs, we performed KEGG enrichment analysis. In the two groups, 2,328 genes were enriched in 329 KEGG pathways. The 20 pathways that were the most enriched included reproduction-related pathways such as the dopaminergic synapse, circadian entrainment, MAPK signaling pathway, chemokine signaling pathway, glutamatergic synapse, and cAMP signaling pathway (Fig. 4 A), which were mainly concentrated in five primary pathways: genetic information processing, environmental information processing, cellular processes, metabolism, and organismal systems (Fig. 4 B). Among them, the ones that had the most genes were transport and catabolism in the cellular process (309), signal transduction in the environmental information processing (587 genes), translation in genetic information processing (205), global and overview maps in metabolism (486 genes), and immune system in organismal systems (362) (Fig. 4 C). Based on the above results, we speculated that the DEGs are involved in the physiological regulation of sheep reproduction through crucial metabolic pathways, such as the MAPK signaling pathway and cAMP signaling pathway (Table S4 ). KEGG Metabolic Pathway of the DEGs in the High Reproduction Group Further KEGG enrichment analysis of up- and down-regulated genes in the high reproduction group, the top 20 pathways of the upregulated genes related reproduction, such as the ErbB signaling pathway, the cAMP signaling pathway, GABA ergic synapses, glutamatergic synapses, and dopaminergic synapses (Fig. 5 A and 5 B, Table S5 ). The top 20 KEGG pathways of the downregulated genes included protein processing in the endoplasmic reticulum, metabolic pathways, and biosynthesis of nucleic acid sugars, amino sugars, and nucleotide sugars (Fig. 5 C and 5 D, Table S5 ). We speculated that the DEGs participate in the physiological regulation of reproduction through major metabolic pathways, such as the ErbB signaling pathway and cAMP signaling pathway. Analysis of the Constructed DEGs Regulatory Network To further explore the interactions between DEGs, the top 12 pathways most associated with reproduction were selected, 375 DEGs were screened, and an interaction network of genes was constructed using the STRING database and Cytoscape software. It included 344 nodes and 2,879 edges. Then they were arranged according to the degree value in Cytoscape software. The 10 core genes with the most neighbors were PRKACB , MAPK1 , CAMK2D , PIK3CB , GNAI3 , RAC1 , PTK2 , ITGB1 , PRKCB , MAPK10 , and MAPK13 (Fig. 6 A). Based on the GO annotations and KEGG pathways of the core genes, Cytoscape software was used to construct a regulatory network of the core target genes involved in sheep reproductive physiology (Fig. 6 B). The core genes mainly regulate the physiological processes of sheep reproduction through the MAPK signaling pathway, the GnRH signaling pathway, and glutamatergic synapses pathway (Table S6 ). Validation of the Transcriptome Sequence via Fluorescence Quantitative PCR To validate the accuracy of the transcriptome sequencing results, we randomly selected eight DEGs ( INPP4B , AKAP4 , PDE10A , MCTP1 , PRKCB , LHFPL6 , CAMK2D , and ELMO1 ) for validation by qRT-PCR. The results showed that the genes in the high reproduction group were significantly upregulated over the low reproduction group, and the trend of the qRT-PCR results was consistent with that of the RNA-Seq results (Fig. 7 ). Discussion Studies on high-throughput transcriptome sequencing technology and high fertility gene have been reported in domestic animals. Third-generation sequencing technology provides a new and more effective method for large-scale transcriptome sequencing studies. In this work, small-tailed Han sheep and Wadi sheep were studied, and the DEGs associated with fecundity in pituitary tissues were screened. A total of 7123 DEGs were found between the high reproduction group and low reproduction group, which were involved mainly in the mTOR signaling pathway, the PI3K/Akt signaling pathway, the cAMP signaling pathway, and the MAPK signaling pathway (Fig. 4 , Table S4 ). This study identified PRKACB , MAPK1 , CAMK2D , PIK3CB , GNAI3 , RAC1 , PTK2 , ITGB1 , PRKCB , MAPK10 , and MAPK13 as candidate genes affecting sheep reproduction and development (Fig. 6 ). These genes and pathways may play roles in sheep reproductive development and ovulation. These findings will help us better understand the mechanism by which the pituitary gland regulates sheep reproductive performance. Some studies have shown that the cAMP signaling pathway plays a critical role in the pituitary gland, regulating cell growth and proliferation, and hormone synthesis and release [ 6 , 7 ]. cAMP is a second messenger present in oocytes, high levels of cAMP have an inhibitory effect on the resumption of meiosis in mammalian oocytes [ 8 ]. In this study, protein kinase A catalytic subunit β (PRKACB) was enriched. PRKACB encodes one of the catalytic subunits of cAMP-activated protein kinase A (PKA) and is involved in many cellular processes, including cell proliferation, differentiation, apoptosis, gene transcription and metabolism [ 9 , 10 ]. During meiosis, cAMP plays an important regulatory role. The PKA regulatory subunit binds to it to release the active catalytic subunit of PKA, which activates PKA to promote the phosphorylation of substrates, thereby blocking the recovery of oocyte meiosis. Therefore, we speculated that PRKACB may regulate sheep reproduction by controlling hormone synthesis and oocyte meiosis. The MAPK signaling pathway is an important pathway in eukaryotic signaling networks. MAPK is a serine/threonine-protein kinase, The MAPK is a key signaling pathway that regulates various physiological processes, such as cell proliferation, differentiation, and apoptosis, which involved in critical physiological processes, such as embryonic stem cell differentiation, oocyte meiosis, cell cycle control, chromatin structure regulation, chromatin remodeling, fertilization, and implantation [ 11 , 12 ]. Prolactin, secreted by the pituitary gland, has an inhibitory effect on the MAPK signaling pathway, and the MAPK signaling pathway plays a regulatory role in follicle development and oocyte meiotic cell cycle progression [ 13 , 14 ]. The mitogen-activated protein kinase 1 ( MAPK1 ) gene is a member of the MAP kinase family, MAPK1 is activated by the luteinizing hormone receptor secreted by pituitary cells, and the protein is phosphorylated in granulosa cells, where it mediates oocyte maturation [ 15 ]. Many DEGs identified in this study belonged to the MAPK signaling pathway (Table S4 ). We speculate that these genes influence reproductive physiology in sheep by regulating the MAPK signaling pathway through prolactin and luteinizing hormone secreted from the pituitary gland, which in turn regulates follicular development and oocyte division. The PIK3CB gene is expressed in both follicle wall granulosa cells and oocytes and is involved in the PI3K/Akt-mediated regulation of follicle development [ 16 ]. The mTOR pathway affects the secretion of growth hormone by interacting with the PI3K and Akt pathways [ 17 ]. The PI3K/Akt/mTOR signaling pathway can regulate oocyte growth,and the mTOR signaling pathway affects the maturation rate of oocytes in a concentration-dependent manner, which in turn affects the reproductive traits of sheep. In this study, the PIK3CB gene was identified as a core gene in the regulatory network involved in the PI3K/Akt/mTOR signaling pathway through gene interaction (Fig. 6 , Table S4 ). Previous studies have also reported that the PIK3CB gene interacts with multiple candidate genes, and the PIK3CB gene was identified as an important gene affecting the reproductive traits of Chinese Holstein cattle [ 18 ]. Therefore, we speculated that the PIK3CB gene affects the secretion of pituitary growth hormone through protein-gene interactions, which in turn affects the PI3K/Akt/mTOR signaling pathway to regulate ovulation in sheep. RAC1 is involved in the regulation of many reproductive activities, including embryo implantation, fixation of mammalian oocytes, meiotic spindle stability, and morphogenesis of embryonic epithelial cells [ 19 , 20 ]. At the same time, the RAC1 protein is expressed in human ovaries, chicken follicles and sheep ovaries, and regulates the formation of primary follicles by promoting the transcription of GDF9 and BMP15 [ 21 ]. In this study, GO and KEGG analyses revealed that RAC1 was annotated in the ErbB signaling pathway, the MAPK signaling pathway and the Notch signaling pathway, and participated in the growth and development of follicles (Appendices 3 and 4). RAC1 gene played a regulatory role as a core gene in the regulatory network through gene interactions. Therefore, we speculated that RAC1 regulates the formation of follicles and oocytes and thus affects the reproductive process of sheep by activating the MAPK signaling pathway and the Notch signaling pathway. In this study, several candidate genes were identified, such as PRKACB , MAPK1 , CAMK2D , PIK3CB , GNAI3 , RAC1 , PTK2 , ITGB1 , PRKCB , MAPK10 , and MAPK13 , that could affect reproductive traits. In addition, some signaling pathways that regulate the reproductive process, such as mTOR, PI3K-Akt, cAMP, and MAPK, were also significantly enriched, suggesting that they may play important roles in the reproductive traits of sheep. Whether the candidate genes screened in this study are the key genes regulating sheep reproductive traits still needs further verification in livestock populations, but the candidate genes we found can, to a certain extent, provide a favourable basis for the selection of individual reproductive performance of sheep in actual production. Conclusions This study successfully constructed six libraries of high and low reproduction varieties, screened a total of 26,067 genes, and identified 7,123 DEGs, 3,551 of them upregulated in the high reproduction group and 3,572 upregulated in the low reproduction group. The DEGs were enriched in a total of 5,930 GO terms and 329 KEGG pathways. Candidate genes that affect reproductive traits were screened, including PRKACB , MAPK1 , CAMK2D , PIK3CB , GNAI3 , RAC1 , PTK2 , ITGB1 , PRKCB , MAPK10 , and MAPK13 . This study can provide new reference for the study of high breeding traits in sheep and can provide theoretical support for the genetic resource conservation and breeding of sheep. Methods Sample Collection and Ethics Statement on Experimental Animals Small-tailed Han ewes (high-reproduction group, from the national small-tailed Han sheep conservation farm) and Wadi ewes (low-reproduction group, from the original Wadi sheep breeding farm in Shandong Province) were chosen as the research subjects. Three small-tailed Han sheep and three Wadi sheep in healthy condition with similar weight and age were randomly selected. The animals were sacrificed without pain. Their pituitary tissues were collected, placed in RNase-free cryopreservation tubes, and stored in liquid nitrogen tanks for further use. All experiments were approved by the Animal Care and Use Committee of Shandong Agricultural University. ONT Library Construction and Sequencing Total RNA from pituitary tissue samples was extracted using the TRIzol method. The purity of the RNA was detected using a Nanodrop spectrophotometer. The RNA concentration was quantified using a Qubit, and quality control was performed by agarose gel electrophoresis. Target mRNAs were reverse-transcribed with oligo-DT as the primer. The full-length cDNA was amplified by low-cycle PCR and purified using AMPure beads, and adapters were sequenced (including protein motors) to build the sequencing library. The library was loaded into the R9.4 sequencing chip and sequenced at Wuhan Beina Technology Co., Ltd., using a PromethION sequencer (Oxford Nanopore Technologies, Oxford, UK). Quality Control and Statistics of the Sequencing Data The raw sequencing data were converted to FASTQ format by GUPPY software (version: 5.0.16). To perform quality control on the ONT raw sequencing data, the original FASTQ data were filtered according to the criteria of a quality value less than 7 and an off-length less than 50 bp to obtain clean reads using NanoFilt software (version: 2.8.0; parameters: -q 7 -l 50) [De et al., 2018]. SeqKit (version: 0.12.0; parameter: default) [ 22 ] was used for statistical analysis and subsequent analysis. The full-length sequences in the valid sequencing data were identified using Pychopper (version: 2.4.0; parameters: -Q 7 -z 50) and filtered using NanoFilt (version: 2.8.0; parameters: -q 7 -l 50) to obtain full-length sequences. The filtered full-length sequences were aligned with the reference genes by using minimap2 (version: 2.17-r941; parameters: 4-ax splice-uf-k14) [ 23 ]. Gene alignment results were analyzed with samtools (version: 1.11; parameters: flagstat) [ 24 ]. Identification of Differentially Expressed Genes (DEGs) To make the estimated gene expression levels comparable between different genes and different experiments, the transcripts per million (TPM) was used as an indicator to measure the expression level. Gene expression quantification was performed using salmon (version: 1.4.0) [ 25 ]. Using the read count data of gene expression levels in each sample obtained from expression quantification, differential expression analysis was performed by DESeq2 (version: 1.26.0) [ 26 ]. The screening thresholds were a P value 1. To calculate TPM, for each gene, the read count value is divided by the length (in kilobases) of the gene to obtain the reads per kilobase (RPK) of the gene. All the RPK values in the sample were calculated and divided by 1,000,000 to obtain the million scaling factor. The RPK value was divided by the million scaling factor to obtain the TPM. Since the sum of all TPMs in each sample is the same when using TPM, it is easier to compare the proportion of reads in each sample that map to genes. Functional Annotation and Enrichment Analysis of the DEGs Statistical methods were used for enrichment analysis, and clusterProfiler (version: 3.14.3) [ 27 ] was used to identify Gene Ontology (GO) ( http://geneontology.org ) [ 28 ], Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ( https://www.genome.jp/kegg/pathway ) [ 29 ] where the DEGs were significantly enriched relative to all the annotated genes. GO describes gene function from three classifications: cellular component (CC), molecular function (MF), and biological process (BP). DEGs were classified according to the KEGG metabolic pathways in which they participated. Construction of the Regulatory Network Based on the GO and KEGG enrichment annotation results, the STRING database ( https://string-db.org/ ) was used for protein‒protein interaction (PPI) analysis of the screened DEGs. Cytoscape software (version 3.10.1, https://cytoscape.org/ ) was used to construct the PPI network. The degree of connectivity of each node was calculated using Cytoscape’s plug-in Cytohubba, and the gene of the node with the most neighbors was defined as the core gene. Validation of DEGs by Fluorescence Quantitative PCR DEGs were randomly selected to verify the accuracy of the transcriptome sequencing data by qRT-PCR. Based on the sheep gene sequence information at the National Center for Biotechnology Information (NCBI), the primer sequences were synthesized by Primer Design Software Premier 5, and the primers were validated using BLAST software on the NCBI website. The following PCR program was used: predenaturation at 94°C for 30 s, denaturation at 95°C for 5 s, and extension at 60°C for 30 s. GAPDH was chosen as the internal reference gene, and three independent biological replicates were run for each group. Gene expression levels were calculated using the 2 −ΔΔCt method, and all data are expressed as mean ± standard error. Abbreviations ONT Oxford Nanopore Technologies GO gene ontology KEGG Kyoto Encyclopedia of Genes and Genomes DEGs Differentially Expressed Genes BMPRIB Bone morphogenetic protein receptor IB BMP15 Bone morphogenetic protein 15 GDF9 Growth differentiation factor 9 NCBI National Center for Biotechnology Information RPK Reads per Kilobase qRT-PCR Real-time Quantitative PCR TPM Transcripts per Million cDNA Complementary DNA RNA Ribonucleic Acid PRKACB Protein kinase A catalytic subunitβ CAMK2D Calcium/Calmodulin Dependent Protein Kinase 2 delta PIK3CB Phosphatidylinositol-4,5-bisphosphate 3-Kinase Catalytic Subunit beta GNAI3 G Protein Subunit alpha i3 RAC1 Rac family small GTPase 1 PTK2 Protein tyrosine kinase 2 ITGB1 Integrin Subunit beta 1 PRKCB protein kinase C, beta Declarations Ethics approval and consent to participate All animal experiments were conducted under the supervision of Animal Care and Use Ethics Committee of Shandong Agricultural University, and all efforts were made to minimize suffering. All trials have gained the knowledge and consent from all owners of sheep. Consent for publication Not applicable. Availability of data and material Data is provided within the manuscript or supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This study was financially supported by grants from the National Key Research and Development Program of China (2021YFD1200901), and Shandong Provincial Modern Agriculture Industry Technology System (SDAIT-22-15). The funding body did not exert influence on the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Author Contributions X.X and J.Z.B have made substantial contributions to the conception, design of the work; J.L the acquisition, analysis, W.T and Z.D.J interpretated data; L.Z.H performed the software used in the work; X.X.M and C.T.L approved the accuracy or integrity of any part of the work; X.X and L.F have drafted the work. Competing interests The authors declare that there are no conflicts of interest. Acknowledgments The authors express their gratitude to their colleagues for the support in sample collection. We are also grateful to small-tailed Han Sheep Breeding Farm in Jiaxiang County of Shandong Province and Shandong Binzhou Animal Science & Veterinary Medicine Academy for providing raw materials; as well as for Institute of Animal biotechnology in Xinjiang academy of animal sciences providing kind assistance in the data analysis and mining. References Montossi F, Font-i-Furnols M, del Campo M, San Julián R, Brito G, Sañudo C. 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ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:277–80. Tables Table.1 Sequencing data information and reference genome statistics results. Group Sample Raw reads Clean reads N50（bp） Total reads Reads mapped on genomics Map rate HP group X1 8,606,523 8,167,307 917 6,212,188 5,238,896 84.33% X2 9,885,400 9,581,802 732 7,501,754 6,317,797 84.22% X3 8,981,989 8,630,100 866 7,026,655 6,111,733 86.98% LP group W1 5,647,518 5,444,801 1,054 4,354,047 4,259,351 97.83% W2 5,273,049 5,030,988 1,039 3,983,982 3,932,003 98.70% W3 5,207,590 4,925,563 1,244 3,793,277 3,711,972 97.86% Note: HP group represents the high reproduction group, LP group represents the low reproduction group. X1, X2, X3 represents the three repetitions in HP group, W1, W2, W3 represents the three repetitions in HP group. map_rate is the ratio of the comparison to the reference genome. Additional Declarations No competing interests reported. Supplementary Files TableS1.Differentialexpressionanalysisofgenes.xlsx Table S1. Differential expression analysis of genes. TableS2.GOenrichmentanalysisofdifferentialgenes.xlsx Table S2. GO enrichment analysis of differential genes. TableS3.GOannotationandenrichmentanalysisofpituitaryupanddownregulatedgenesinthehighbreedinggroup..xlsx Table S3. GO annotation and enrichment analysis of pituitary up- and down-regulated genes in the high-breeding group. TableS4.KEGGenrichmentanalysisofdifferentialgenes.xlsx Table S4. KEGG enrichment analysis of differential genes. TableS5.KEGGenrichmentanalysisofpituitaryupanddownregulatedgenesinthehighbreedinggroup.xlsx Table S5. KEGG enrichment analysis of pituitary up- and down-regulated genes in the high-breeding group. TableS6.Coregenenetworkinteractions.xlsx Table S6. Core gene network interactions. TableS7.Ethicalapproval.pdf Table S7. Ethical approval. TableS8.PrimerinformationusedintheqRTPCR..xlsx Table S8. Primer information used in the qRT-PCR. Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 01 Aug, 2024 Editor assigned by journal 31 Jul, 2024 Submission checks completed at journal 31 Jul, 2024 First submitted to journal 27 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4812389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334672995,"identity":"2b7a586b-63a0-4199-855a-86395e188365","order_by":0,"name":"Xue Xiao","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Xiao","suffix":""},{"id":334672996,"identity":"a3e98616-ca6e-4ba3-a915-bec98cce9fcd","order_by":1,"name":"Lin Ju","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ju","suffix":""},{"id":334672997,"identity":"65386c0e-b652-4ff1-a2b5-afecffac1873","order_by":2,"name":"Zhibin Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCYaEAx8MbOQYGBJAXGbitBycUZBmTJIWBmaeD4cSG4jWIj+74eFhHoMD6fPbk59JMFRYJzawnz2AVwvjnAMJB+cY3MndcOaZmQTDmfTEBp68BLxamCUSEg68MXiWu0EiwUyCse1wYoMEjwFeLWwgLTwGh9PlZ6R/k2D8R4QWHqCWg0AtCQw3coC2NBChRUIG6JcZBmmGG868KbZIOJZu3MaTg1+L/Oye5A8f/tjIy7enb7zxocZatp/9DH4tQKclINggJhsB9UDAfoCwmlEwCkbBKBjZAACOpUqg9LDr2QAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Ji","suffix":""},{"id":334672998,"identity":"b5062624-19df-4fb0-af76-66e6af231fcb","order_by":3,"name":"Tong Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Wang","suffix":""},{"id":334672999,"identity":"42137527-dfc3-4b13-b91d-fca5b7f65b63","order_by":4,"name":"Dejie Zhu","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dejie","middleName":"","lastName":"Zhu","suffix":""},{"id":334673000,"identity":"730f2ce7-89ab-4b5f-843b-8d661f596385","order_by":5,"name":"Zhonghui Li","email":"","orcid":"","institution":"Xinjiang academy of animal sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhonghui","middleName":"","lastName":"Li","suffix":""},{"id":334673001,"identity":"1eee07a9-4665-4eb8-9bdc-64cc82b739ec","order_by":6,"name":"Xinming Xu","email":"","orcid":"","institution":"Xinjiang academy of animal sciences","correspondingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Xu","suffix":""},{"id":334673002,"identity":"a443e301-1492-46a3-b55d-959a7d1d4403","order_by":7,"name":"Tianle Chao","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tianle","middleName":"","lastName":"Chao","suffix":""},{"id":334673003,"identity":"474bd515-8005-4a24-b6bf-22cc90a67870","order_by":8,"name":"Fen Li","email":"","orcid":"","institution":"Shandong Binzhou Animal Science \u0026 Veterinary Medicine Academy Country","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-27 09:48:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4812389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4812389/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11732-5","type":"published","date":"2025-06-05T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63370021,"identity":"3e744eed-e89c-46cd-8895-903343bbd823","added_by":"auto","created_at":"2024-08-27 11:47:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and expression analysis of differential gene.\u003c/strong\u003e (A) Box plots of gene expression of all samples; (B) Density distribution of gene expression of all samples; (C) Statistical plots of the number of differentially expressed genes among different groups obtained by sequencing; (D) MA plots of differential analysis of gene expression levels; (E) Volcano plots of differential analysis of gene expression levels; (F) Sample clustering plots of differentially expressed genes.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/2b487f3644cef27803c578c9.png"},{"id":63372077,"identity":"159cc8a1-9e13-4a82-bf8f-aad6c99aaca5","added_by":"auto","created_at":"2024-08-27 12:03:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO annotation and enrichment analysis of differentially expressed genes in the pituitary gland.\u003c/strong\u003e (A) Histogram of the distribution of top 20 GO terms in different GO Classes; (B) Bubble chart of top 20 GO terms significantly enriched of differentially expressed genes; (C) Statistical chart of top 20 GO terms enriched for differentially expressed genes in different GO Classes.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/0a98c97936dad8607e987f48.png"},{"id":63370025,"identity":"8e4cf9ec-6bc0-4175-a7cc-b5f26fac04ba","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO annotation and enrichment analysis of pituitary up- and down-regulated genes in the high-reproduction group.\u003c/strong\u003e (A) Histogram of enrichment analysis results of top 20 GO terms enriched by up-regulated differential genes in different GO classes; (B) Bubble plots of the GO terms significantly enriched by up-regulated differential genes; (C) Histogram of top 20 GO terms enriched by down-regulated differential genes in different GO classes; (D) Bubble plots of top 20 GO terms significantly enriched for down-regulated differential genes.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/dfe0652ced5f00a0d5553d4b.png"},{"id":63370022,"identity":"c044b7fd-37a7-413c-b19e-148be5da2425","added_by":"auto","created_at":"2024-08-27 11:47:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG enrichment analysis of differentially expressed genes in the pituitary gland. \u003c/strong\u003e(A) Histogram of top 20 KEGG Pathways significantly enriched for differentially expressed genes; (B) Statistical graph of the classification of KEGG Pathways for differentially expressed genes; (C) Bubble graph of KEGG Pathways for differentially expressed genes in different KEGG Classes.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/edc240a3e941d89c3e96b0cc.png"},{"id":63370035,"identity":"f0d98ea6-2d92-41c0-b70b-87cea54c82cf","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":220433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG enrichment analysis of pituitary up-regulated and down-regulated genes in the high-reproduction group. \u003c/strong\u003e(A) Histogram of the distribution of top 20 KEGG Pathways significantly enriched for down-regulated differential genes in different KEGG Classes; (B) Bubble plots of top 20 KEGG Pathways enriched for down-regulated differential genes; (C)Histogram of the distribution results of top 20 KEGG Pathways significantly enriched in up-regulated differential genes in different KEGG Classes; (D) Bubble plots of top 20 KEGG Pathway enriched for up-regulated differential gene.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/0386bccaadf5dcb23cfd0487.png"},{"id":63370033,"identity":"67ff549b-bbb2-470d-890b-9e32cf52d9c3","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed gene network.\u003c/strong\u003e (A) Map of PPI interactions network; (B) Regulatory network of core gene.\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/c430544dc568600733e4924d.png"},{"id":63370030,"identity":"4f64ee92-fe08-4d27-8ba5-6639ce1b7c3c","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of differentially expressed genes in the pituitary gland.\u003c/strong\u003e W represents the low-reproduction group, X represents the high-reproduction group\u003c/p\u003e","description":"","filename":"OnlineFig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/b6c4f7a9e58aa718ab23ca48.png"},{"id":84242545,"identity":"5fc56abb-420b-44a8-a251-e2cb8eee5237","added_by":"auto","created_at":"2025-06-09 16:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2608013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/60457368-2f69-4fb3-bb5a-0fdb31dd1fbf.pdf"},{"id":63370031,"identity":"9d5e45f5-e9fc-4125-952d-565203b15d02","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2626138,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Differential expression analysis of genes.\u003c/p\u003e","description":"","filename":"TableS1.Differentialexpressionanalysisofgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/0830984a7a10bc2514bfb7e4.xlsx"},{"id":63372078,"identity":"0c6e20e6-bc7a-49c1-9ef5-2bc08aaf282b","added_by":"auto","created_at":"2024-08-27 12:03:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":154603,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2. GO enrichment analysis of differential genes.\u003c/p\u003e","description":"","filename":"TableS2.GOenrichmentanalysisofdifferentialgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/e5bb2c7fee129ad73bf7524e.xlsx"},{"id":63371398,"identity":"7d27f27f-1a59-44b9-8e03-cbd87958bfc9","added_by":"auto","created_at":"2024-08-27 11:55:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":699479,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3. GO annotation and enrichment analysis of pituitary up- and down-regulated genes in the high-breeding group.\u003c/p\u003e","description":"","filename":"TableS3.GOannotationandenrichmentanalysisofpituitaryupanddownregulatedgenesinthehighbreedinggroup..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/05b5bad2f251c36dfb644da7.xlsx"},{"id":63371396,"identity":"5530201a-6af9-44f1-a99d-cb6451b41496","added_by":"auto","created_at":"2024-08-27 11:55:14","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":79353,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4. KEGG enrichment analysis of differential genes.\u003c/p\u003e","description":"","filename":"TableS4.KEGGenrichmentanalysisofdifferentialgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/4a372ea6d347d515d6f064d9.xlsx"},{"id":63372079,"identity":"12026438-0ae6-4f1c-964a-35ac57f113aa","added_by":"auto","created_at":"2024-08-27 12:03:14","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":107800,"visible":true,"origin":"","legend":"\u003cp\u003eTable S5. KEGG enrichment analysis of pituitary up- and down-regulated genes in the high-breeding group.\u003c/p\u003e","description":"","filename":"TableS5.KEGGenrichmentanalysisofpituitaryupanddownregulatedgenesinthehighbreedinggroup.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/d9dccf05970111c0fc5770af.xlsx"},{"id":63371400,"identity":"b919d9a5-e47e-4969-9404-fd9a643437ca","added_by":"auto","created_at":"2024-08-27 11:55:14","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":194389,"visible":true,"origin":"","legend":"\u003cp\u003eTable S6. Core gene network interactions.\u003c/p\u003e","description":"","filename":"TableS6.Coregenenetworkinteractions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/99ed033825521ba33ed1afe4.xlsx"},{"id":63371402,"identity":"762c2c2f-82eb-4b39-b2e4-3994939355cd","added_by":"auto","created_at":"2024-08-27 11:55:14","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":783700,"visible":true,"origin":"","legend":"\u003cp\u003eTable S7. Ethical approval.\u003c/p\u003e","description":"","filename":"TableS7.Ethicalapproval.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/ae68e9d883b11cf63eb2eb0a.pdf"},{"id":63370029,"identity":"03b5c7b4-1757-4810-a0ab-c7df771d90a7","added_by":"auto","created_at":"2024-08-27 11:47:14","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12351,"visible":true,"origin":"","legend":"\u003cp\u003eTable S8. Primer information used in the qRT-PCR.\u003c/p\u003e","description":"","filename":"TableS8.PrimerinformationusedintheqRTPCR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4812389/v1/027f8ae44c4f814bc3001a8d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scanning and Mining of High Fecundity Genes by Oxford Nanopore Technologies (ONT) in Sheep (Ovis aries) Pituitary","fulltext":[{"header":"Background","content":"\u003cp\u003eAmong grass-fed livestock, sheep have a high feed conversion rate, a wide range of food intakes and adaptabilities, and the ability to make full use of resources that other livestock cannot. In recent years, with social and economic development, the consumption of mutton has increased, so new breeds of sheep with fast growth, high fecundity, and good carcass quality is urgently needed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Small-tailed Han sheep have excellent reproductive traits, such as precocious maturity, perennial estrus, and multiple lambs, making them one of the world-renowned high-reproduction sheep breeds. Wadi sheep are local breeds in Shandong Province that mature early but have a relatively low reproductive rate.\u003c/p\u003e \u003cp\u003eOxford Nanopore Technologies (ONT) is an emerging third-generation single-molecule sequencing technology. It is a platform for MinION sequencing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that can be used to comprehensively and rapidly obtain information on virtually all transcripts of a particular organ or tissue of a species in a particular state, and has seen wide usage in genomics research. Reproduction is a complex process. Traits such as ovulation rate and litter size are influenced by the inheritance of many minor genes and some major genes. Genes associated with litter size, such as bone morphogenetic protein receptor IB (BMPRIB), bone morphogenetic protein 15 (BMP15), and growth differentiation factor 9 (GDF9), are located on sheep chromosomes 6, X, and 5 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Reproductive traits are important economic traits in sheep farming and are affected by many factors, but the molecular regulatory mechanisms in pituitary tissue need to be further studied.\u003c/p\u003e \u003cp\u003eThe pituitary gland is an important endocrine organ that regulates estrus and reproduction in sheep mainly through hormone synthesis and secretion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies on reproduction traits have focused mainly on folliculogenesis and ovulation in sheep with different fecundities, and few systematic analyses of the mRNAs expressed in the pituitary have been performed. Therefore, in this study, sequencing analysis of the pituitary glands of Wadi sheep and small-tailed Han sheep of different fecundities was performed. The findings enriche the genetic information in the pituitary gland of sheep and provides candidate molecules at the gene level that may regulate multiple fetuses in sheep. We hope that this study will provide a theoretical basis for analyzing sheep reproductive traits and improving sheep production performance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRNA Quality Detection and Sequencing Quality Analysis\u003c/h2\u003e \u003cp\u003eThe ONT method was used to sequence the pituitary tissues of small-tailed Han sheep (high reproduction) and Wadi sheep (low reproduction), and three biological samples were run in each group. The obtained data were shown in Table\u0026nbsp;1. The original data of the high reproduction group (X1, X2, X3) and the low reproduction group (W1, W2, W3) included 8,606,523, 9,885,400, 8,981,989, 5,647,518, 5,273,049, and 5,207,590 reads, respectively. After removing reads with adapters and low quality, there were 8,167,307, 9,581,802, 8,630,100, 5,444,801, 5,030,988, and 4,925,563 clean reads, respectively. And the proportion of clean reads after filtering was more than 90%. The length of N50 of the six libraries obtained from sequencing were 917, 732, 866, 1,054, 1,039, and 1,244 bp, respectively. As can be seen from the Table\u0026nbsp;1, the length of N50 in the high reproduction group clearly differed from that of N50 in the low reproduction group, which could be used in the functional analysis of the differentially expressed genes (DEGs). The alignment rates of the identified full-length sequences to the reference genome were 84.33%, 84.22%, 86.98%, 97.83%, 97.83%, 98.7%, and 97.86%, respectively. These results indicated that the six constructed libraries and the data were of good quality for use in further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Expression Analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe level of gene expression is regulated by various factors. The amount of transcribed mRNA varies between tissues and growth stages. In this study, a box plot was drawn for each sample. The overall expression trend of the three pituitary samples in the high reproduction group was the same, and the distribution trend of the overall expression levels of the three pituitary samples in the low reproduction group was consistent. The overall gene expression levels of the high reproduction group were significantly lower than those in the low reproduction group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Comparing the expression levels of all the genes between the high and low reproduction groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), there were 7,123 DEGs between the two groups, 3,551 being upregulated and 3,572 downregulated in the high reproduction group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo more intuitively understand the overall distribution of the DEGs, we used the log\u003csub\u003e2\u003c/sub\u003e(fold change), basemean, and p\u003csub\u003eadj\u003c/sub\u003e (the \u003cem\u003ep\u003c/em\u003e value after multiple-hypothesis testing correction) to construct the MA plot and volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From those we can clearly see that the genes obtained by sequencing show a clear differential distribution of up- and down-regulation among the different groups. The cluster analysis based on the DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF) showed that the individuals in the high reproduction group (X1, X2, X3) clustered together and the individuals in the low reproduction group (W1, W2, W3) clustered together. The expression patterns of the DEGs between two groups were significantly different. We speculated that the DEGs found here were involved in the regulation of reproductive performance in sheep.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGO Enrichment Analysis of DEGs\u003c/h2\u003e \u003cp\u003eFor the 7,123 DEGs obtained from sequencing, GO annotation was performed for the three categories of molecular function, cellular components, and biological processes. Among them, 4,425 genes were annotated in the GO database, and a total of 5,930 terms were enriched, of which 3,654 (62%) were classified under biological processes, 789 (13%) were classified under cellular components, and 1,496 (25%) were classified under molecular functions. In the two groups, the most significantly enriched biological processes were regulation of transcription and DNA templated, followed by signal transduction and translation; the most significantly enriched molecular functions were metal ion binding, ATP binding, and calcium ion binding. The enrichment of cellular components was mainly concentrated in the cytoplasm, nucleus, plasma membrane, and integral component of membrane. The number and significant expression status of genes enriched for each enrichment entry are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Based on the above results, we speculated that the DEGs between the two groups play important biological roles in different parts of the cell through transcriptional regulation, signal transduction, catalytic binding, and other pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGO Function Annotation of the DEGs in the High Reproduction Group\u003c/h2\u003e \u003cp\u003eBy annotating 1,622 upregulated genes in the high reproduction group into the GO database, a total of 2,866 terms were enriched. Among them, 1,711 terms were enriched for biological processes, 441 terms were enriched for cellular components, and 714 terms were enriched for molecular functions. Significant enrichment in biological processes included intracellular signal transduction, regulation of ion transmembrane transport, and nervous system development. The main enriched cellular components included the membrane and cytoskeleton. The main enriched molecular functions included ATP binding, calciumion binding, and protein kinase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). By annotating 2,803 downregulated genes in the high reproduction group to the GO database, a total of 4,716 terms were enriched, including 2,878 biological process, 650 cellular component, and 1,188 molecular function. The biological processes mainly included translation and intracellular protein transport; the cellular components mainly included the cytoplasm, cytosol, and endoplasmic reticulum membrane; and the molecular functions mainly included structural constituents of ribosomes, GTP binding, identical protein binding, and GTPase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). We speculated that the DEGs may regulate the physiological processes of sheep reproduction through biological processes such as cell signal transduction, ATP binding, and protein transport.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Enrichment Analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo further identify the main functions of the DEGs, we performed KEGG enrichment analysis. In the two groups, 2,328 genes were enriched in 329 KEGG pathways. The 20 pathways that were the most enriched included reproduction-related pathways such as the dopaminergic synapse, circadian entrainment, MAPK signaling pathway, chemokine signaling pathway, glutamatergic synapse, and cAMP signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), which were mainly concentrated in five primary pathways: genetic information processing, environmental information processing, cellular processes, metabolism, and organismal systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among them, the ones that had the most genes were transport and catabolism in the cellular process (309), signal transduction in the environmental information processing (587 genes), translation in genetic information processing (205), global and overview maps in metabolism (486 genes), and immune system in organismal systems (362) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Based on the above results, we speculated that the DEGs are involved in the physiological regulation of sheep reproduction through crucial metabolic pathways, such as the MAPK signaling pathway and cAMP signaling pathway (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Metabolic Pathway of the DEGs in the High Reproduction Group\u003c/h2\u003e \u003cp\u003eFurther KEGG enrichment analysis of up- and down-regulated genes in the high reproduction group, the top 20 pathways of the upregulated genes related reproduction, such as the ErbB signaling pathway, the cAMP signaling pathway, GABA ergic synapses, glutamatergic synapses, and dopaminergic synapses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The top 20 KEGG pathways of the downregulated genes included protein processing in the endoplasmic reticulum, metabolic pathways, and biosynthesis of nucleic acid sugars, amino sugars, and nucleotide sugars (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). We speculated that the DEGs participate in the physiological regulation of reproduction through major metabolic pathways, such as the ErbB signaling pathway and cAMP signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the Constructed DEGs Regulatory Network\u003c/h2\u003e \u003cp\u003eTo further explore the interactions between DEGs, the top 12 pathways most associated with reproduction were selected, 375 DEGs were screened, and an interaction network of genes was constructed using the STRING database and Cytoscape software. It included 344 nodes and 2,879 edges. Then they were arranged according to the degree value in Cytoscape software. The 10 core genes with the most neighbors were \u003cem\u003ePRKACB\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, \u003cem\u003ePIK3CB\u003c/em\u003e, \u003cem\u003eGNAI3\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eMAPK10\u003c/em\u003e, and \u003cem\u003eMAPK13\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Based on the GO annotations and KEGG pathways of the core genes, Cytoscape software was used to construct a regulatory network of the core target genes involved in sheep reproductive physiology (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The core genes mainly regulate the physiological processes of sheep reproduction through the MAPK signaling pathway, the GnRH signaling pathway, and glutamatergic synapses pathway (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Transcriptome Sequence via Fluorescence Quantitative PCR\u003c/h2\u003e \u003cp\u003eTo validate the accuracy of the transcriptome sequencing results, we randomly selected eight DEGs (\u003cem\u003eINPP4B\u003c/em\u003e, \u003cem\u003eAKAP4\u003c/em\u003e, \u003cem\u003ePDE10A\u003c/em\u003e, \u003cem\u003eMCTP1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eLHFPL6\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, and \u003cem\u003eELMO1\u003c/em\u003e) for validation by qRT-PCR. The results showed that the genes in the high reproduction group were significantly upregulated over the low reproduction group, and the trend of the qRT-PCR results was consistent with that of the RNA-Seq results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies on high-throughput transcriptome sequencing technology and high fertility gene have been reported in domestic animals. Third-generation sequencing technology provides a new and more effective method for large-scale transcriptome sequencing studies. In this work, small-tailed Han sheep and Wadi sheep were studied, and the DEGs associated with fecundity in pituitary tissues were screened. A total of 7123 DEGs were found between the high reproduction group and low reproduction group, which were involved mainly in the mTOR signaling pathway, the PI3K/Akt signaling pathway, the cAMP signaling pathway, and the MAPK signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). This study identified \u003cem\u003ePRKACB\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, \u003cem\u003ePIK3CB\u003c/em\u003e, \u003cem\u003eGNAI3\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eMAPK10\u003c/em\u003e, and \u003cem\u003eMAPK13\u003c/em\u003e as candidate genes affecting sheep reproduction and development (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These genes and pathways may play roles in sheep reproductive development and ovulation. These findings will help us better understand the mechanism by which the pituitary gland regulates sheep reproductive performance.\u003c/p\u003e \u003cp\u003eSome studies have shown that the cAMP signaling pathway plays a critical role in the pituitary gland, regulating cell growth and proliferation, and hormone synthesis and release [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. cAMP is a second messenger present in oocytes, high levels of cAMP have an inhibitory effect on the resumption of meiosis in mammalian oocytes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this study, protein kinase A catalytic subunit β (PRKACB) was enriched. \u003cem\u003ePRKACB\u003c/em\u003e encodes one of the catalytic subunits of cAMP-activated protein kinase A (PKA) and is involved in many cellular processes, including cell proliferation, differentiation, apoptosis, gene transcription and metabolism [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. During meiosis, cAMP plays an important regulatory role. The PKA regulatory subunit binds to it to release the active catalytic subunit of PKA, which activates PKA to promote the phosphorylation of substrates, thereby blocking the recovery of oocyte meiosis. Therefore, we speculated that \u003cem\u003ePRKACB\u003c/em\u003e may regulate sheep reproduction by controlling hormone synthesis and oocyte meiosis.\u003c/p\u003e \u003cp\u003eThe MAPK signaling pathway is an important pathway in eukaryotic signaling networks. MAPK is a serine/threonine-protein kinase, The MAPK is a key signaling pathway that regulates various physiological processes, such as cell proliferation, differentiation, and apoptosis, which involved in critical physiological processes, such as embryonic stem cell differentiation, oocyte meiosis, cell cycle control, chromatin structure regulation, chromatin remodeling, fertilization, and implantation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Prolactin, secreted by the pituitary gland, has an inhibitory effect on the MAPK signaling pathway, and the MAPK signaling pathway plays a regulatory role in follicle development and oocyte meiotic cell cycle progression [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The mitogen-activated protein kinase 1 (\u003cem\u003eMAPK1\u003c/em\u003e) gene is a member of the MAP kinase family, MAPK1 is activated by the luteinizing hormone receptor secreted by pituitary cells, and the protein is phosphorylated in granulosa cells, where it mediates oocyte maturation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Many DEGs identified in this study belonged to the MAPK signaling pathway (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). We speculate that these genes influence reproductive physiology in sheep by regulating the MAPK signaling pathway through prolactin and luteinizing hormone secreted from the pituitary gland, which in turn regulates follicular development and oocyte division.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ePIK3CB\u003c/em\u003e gene is expressed in both follicle wall granulosa cells and oocytes and is involved in the PI3K/Akt-mediated regulation of follicle development [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The mTOR pathway affects the secretion of growth hormone by interacting with the PI3K and Akt pathways [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The PI3K/Akt/mTOR signaling pathway can regulate oocyte growth,and the mTOR signaling pathway affects the maturation rate of oocytes in a concentration-dependent manner, which in turn affects the reproductive traits of sheep. In this study, the \u003cem\u003ePIK3CB\u003c/em\u003e gene was identified as a core gene in the regulatory network involved in the PI3K/Akt/mTOR signaling pathway through gene interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Previous studies have also reported that the \u003cem\u003ePIK3CB\u003c/em\u003e gene interacts with multiple candidate genes, and the \u003cem\u003ePIK3CB\u003c/em\u003e gene was identified as an important gene affecting the reproductive traits of Chinese Holstein cattle [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, we speculated that the \u003cem\u003ePIK3CB\u003c/em\u003e gene affects the secretion of pituitary growth hormone through protein-gene interactions, which in turn affects the PI3K/Akt/mTOR signaling pathway to regulate ovulation in sheep.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRAC1\u003c/em\u003e is involved in the regulation of many reproductive activities, including embryo implantation, fixation of mammalian oocytes, meiotic spindle stability, and morphogenesis of embryonic epithelial cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. At the same time, the RAC1 protein is expressed in human ovaries, chicken follicles and sheep ovaries, and regulates the formation of primary follicles by promoting the transcription of \u003cem\u003eGDF9\u003c/em\u003e and \u003cem\u003eBMP15\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, GO and KEGG analyses revealed that \u003cem\u003eRAC1\u003c/em\u003e was annotated in the ErbB signaling pathway, the MAPK signaling pathway and the Notch signaling pathway, and participated in the growth and development of follicles (Appendices 3 and 4). \u003cem\u003eRAC1\u003c/em\u003e gene played a regulatory role as a core gene in the regulatory network through gene interactions. Therefore, we speculated that \u003cem\u003eRAC1\u003c/em\u003e regulates the formation of follicles and oocytes and thus affects the reproductive process of sheep by activating the MAPK signaling pathway and the Notch signaling pathway.\u003c/p\u003e \u003cp\u003eIn this study, several candidate genes were identified, such as \u003cem\u003ePRKACB\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, \u003cem\u003ePIK3CB\u003c/em\u003e, \u003cem\u003eGNAI3\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eMAPK10\u003c/em\u003e, and \u003cem\u003eMAPK13\u003c/em\u003e, that could affect reproductive traits. In addition, some signaling pathways that regulate the reproductive process, such as mTOR, PI3K-Akt, cAMP, and MAPK, were also significantly enriched, suggesting that they may play important roles in the reproductive traits of sheep. Whether the candidate genes screened in this study are the key genes regulating sheep reproductive traits still needs further verification in livestock populations, but the candidate genes we found can, to a certain extent, provide a favourable basis for the selection of individual reproductive performance of sheep in actual production.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study successfully constructed six libraries of high and low reproduction varieties, screened a total of 26,067 genes, and identified 7,123 DEGs, 3,551 of them upregulated in the high reproduction group and 3,572 upregulated in the low reproduction group. The DEGs were enriched in a total of 5,930 GO terms and 329 KEGG pathways. Candidate genes that affect reproductive traits were screened, including \u003cem\u003ePRKACB\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, \u003cem\u003ePIK3CB\u003c/em\u003e, \u003cem\u003eGNAI3\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eMAPK10\u003c/em\u003e, and \u003cem\u003eMAPK13\u003c/em\u003e. This study can provide new reference for the study of high breeding traits in sheep and can provide theoretical support for the genetic resource conservation and breeding of sheep.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eSample Collection and Ethics Statement on Experimental Animals\u003c/h2\u003e \u003cp\u003eSmall-tailed Han ewes (high-reproduction group, from the national small-tailed Han sheep conservation farm) and Wadi ewes (low-reproduction group, from the original Wadi sheep breeding farm in Shandong Province) were chosen as the research subjects. Three small-tailed Han sheep and three Wadi sheep in healthy condition with similar weight and age were randomly selected. The animals were sacrificed without pain. Their pituitary tissues were collected, placed in RNase-free cryopreservation tubes, and stored in liquid nitrogen tanks for further use. All experiments were approved by the Animal Care and Use Committee of Shandong Agricultural University.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eONT Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA from pituitary tissue samples was extracted using the TRIzol method. The purity of the RNA was detected using a Nanodrop spectrophotometer. The RNA concentration was quantified using a Qubit, and quality control was performed by agarose gel electrophoresis. Target mRNAs were reverse-transcribed with oligo-DT as the primer. The full-length cDNA was amplified by low-cycle PCR and purified using AMPure beads, and adapters were sequenced (including protein motors) to build the sequencing library. The library was loaded into the R9.4 sequencing chip and sequenced at Wuhan Beina Technology Co., Ltd., using a PromethION sequencer (Oxford Nanopore Technologies, Oxford, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control and Statistics of the Sequencing Data\u003c/h2\u003e \u003cp\u003eThe raw sequencing data were converted to FASTQ format by GUPPY software (version: 5.0.16). To perform quality control on the ONT raw sequencing data, the original FASTQ data were filtered according to the criteria of a quality value less than 7 and an off-length less than 50 bp to obtain clean reads using NanoFilt software (version: 2.8.0; parameters: -q 7 -l 50) [De et al., 2018]. SeqKit (version: 0.12.0; parameter: default) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used for statistical analysis and subsequent analysis. The full-length sequences in the valid sequencing data were identified using Pychopper (version: 2.4.0; parameters: -Q 7 -z 50) and filtered using NanoFilt (version: 2.8.0; parameters: -q 7 -l 50) to obtain full-length sequences. The filtered full-length sequences were aligned with the reference genes by using minimap2 (version: 2.17-r941; parameters: 4-ax splice-uf-k14) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Gene alignment results were analyzed with samtools (version: 1.11; parameters: flagstat) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Differentially Expressed Genes (DEGs)\u003c/h2\u003e \u003cp\u003eTo make the estimated gene expression levels comparable between different genes and different experiments, the transcripts per million (TPM) was used as an indicator to measure the expression level. Gene expression quantification was performed using salmon (version: 1.4.0) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Using the read count data of gene expression levels in each sample obtained from expression quantification, differential expression analysis was performed by DESeq2 (version: 1.26.0) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The screening thresholds were a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a |log2FoldChange| \u0026gt; 1.\u003c/p\u003e \u003cp\u003eTo calculate TPM, for each gene, the read count value is divided by the length (in kilobases) of the gene to obtain the reads per kilobase (RPK) of the gene. All the RPK values in the sample were calculated and divided by 1,000,000 to obtain the million scaling factor. The RPK value was divided by the million scaling factor to obtain the TPM. Since the sum of all TPMs in each sample is the same when using TPM, it is easier to compare the proportion of reads in each sample that map to genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Annotation and Enrichment Analysis of the DEGs\u003c/h2\u003e \u003cp\u003eStatistical methods were used for enrichment analysis, and clusterProfiler (version: 3.14.3) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used to identify Gene Ontology (GO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org\u003c/span\u003e\u003cspan address=\"http://geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] where the DEGs were significantly enriched relative to all the annotated genes. GO describes gene function from three classifications: cellular component (CC), molecular function (MF), and biological process (BP). DEGs were classified according to the KEGG metabolic pathways in which they participated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the Regulatory Network\u003c/h2\u003e \u003cp\u003eBased on the GO and KEGG enrichment annotation results, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for protein‒protein interaction (PPI) analysis of the screened DEGs. Cytoscape software (version 3.10.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct the PPI network. The degree of connectivity of each node was calculated using Cytoscape\u0026rsquo;s plug-in Cytohubba, and the gene of the node with the most neighbors was defined as the core gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eValidation of DEGs by Fluorescence Quantitative PCR\u003c/h2\u003e \u003cp\u003eDEGs were randomly selected to verify the accuracy of the transcriptome sequencing data by qRT-PCR. Based on the sheep gene sequence information at the National Center for Biotechnology Information (NCBI), the primer sequences were synthesized by Primer Design Software Premier 5, and the primers were validated using BLAST software on the NCBI website. The following PCR program was used: predenaturation at 94\u0026deg;C for 30 s, denaturation at 95\u0026deg;C for 5 s, and extension at 60\u0026deg;C for 30 s. GAPDH was chosen as the internal reference gene, and three independent biological replicates were run for each group. Gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, and all data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eONT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxford Nanopore Technologies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMPRIB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBone morphogenetic protein receptor IB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMP15\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBone morphogenetic protein 15\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDF9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrowth differentiation factor 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRPK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReads per Kilobase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReal-time Quantitative PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscripts per Million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplementary DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRKACB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein kinase A catalytic subunitβ\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAMK2D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcium/Calmodulin Dependent Protein Kinase 2 delta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIK3CB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylinositol-4,5-bisphosphate 3-Kinase Catalytic Subunit beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNAI3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eG Protein Subunit alpha i3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAC1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRac family small GTPase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTK2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein tyrosine kinase 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITGB1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrin Subunit beta 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRKCB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein kinase C, beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted under the supervision of Animal Care and Use Ethics Committee of Shandong Agricultural University, and all efforts were made to minimize suffering. All trials have gained the knowledge and consent from all owners of sheep.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by grants from the National Key Research and Development Program of China (2021YFD1200901), and Shandong Provincial Modern Agriculture Industry Technology System (SDAIT-22-15). The funding body did not exert influence on the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.X and J.Z.B have made substantial contributions to the conception, design of the work; J.L the acquisition, analysis, W.T and Z.D.J interpretated data; L.Z.H performed the \u0026nbsp; software used in the work; X.X.M and C.T.L approved the accuracy or integrity of any part of the work; X.X and L.F have drafted the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to their colleagues for the support in sample collection. We are also grateful to small-tailed Han Sheep Breeding Farm in Jiaxiang County of Shandong Province and Shandong Binzhou Animal Science \u0026amp; Veterinary Medicine Academy for providing raw materials; as well as for Institute of Animal biotechnology in Xinjiang academy of animal sciences providing kind assistance in the data analysis and mining.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMontossi F, Font-i-Furnols M, del Campo M, San Juli\u0026aacute;n R, Brito G, Sa\u0026ntilde;udo C. Sustainable sheep production and consumer preference trends: compatibilities, contradictions, and unresolved dilemmas. Meat Sci. 2013;95:772\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuan K, Li J, Han H, Wei H, Zhao J, Si HA, et al. Review of Huang-huai sheep, a new multiparous mutton sheep breed first identified in China. 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PLoS ONE. 2016;11:e0163962.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:277\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable.1 Sequencing data information and reference genome statistics results.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.947189097103918%\"\u003e\n \u003cp\u003eRaw reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003eClean reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003eN50（bp）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003eTotal reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp\u003eReads mapped on genomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003eMap rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.710391822827939%\" rowspan=\"3\"\u003e\n \u003cp\u003eHP group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003eX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.947189097103918%\"\u003e\n \u003cp\u003e8,606,523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e8,167,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003e917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e6,212,188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp\u003e5,238,896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e84.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003eX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e9,885,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e9,581,802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e7,501,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\"\u003e\n \u003cp\u003e6,317,797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.452830188679245%\"\u003e\n \u003cp\u003e84.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003eX3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e8,981,989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e8,630,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003e866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e7,026,655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\"\u003e\n \u003cp\u003e6,111,733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.452830188679245%\"\u003e\n \u003cp\u003e86.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.710391822827939%\" rowspan=\"3\"\u003e\n \u003cp\u003eLP group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003eW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.947189097103918%\"\u003e\n \u003cp\u003e5,647,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e5,444,801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\"\u003e\n \u003cp\u003e1,054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e4,354,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp\u003e4,259,351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e97.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003eW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e5,273,049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e5,030,988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003e1,039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e3,983,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\"\u003e\n \u003cp\u003e3,932,003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.452830188679245%\"\u003e\n \u003cp\u003e98.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003eW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e5,207,590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e4,925,563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.754716981132075%\"\u003e\n \u003cp\u003e1,244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e3,793,277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\"\u003e\n \u003cp\u003e3,711,972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.452830188679245%\"\u003e\n \u003cp\u003e97.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003eNote: HP group represents the high reproduction group, LP group represents the low reproduction group. X1, X2, X3 represents the three repetitions in HP group, W1, W2, W3 represents the three repetitions in HP group. map_rate is the ratio of the comparison to the reference genome.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sheep, pituitary, fecundity, ONT, sequencing","lastPublishedDoi":"10.21203/rs.3.rs-4812389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4812389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eReproduction is a complex process, which is influenced by the inheritance of many minor genes and some major genes. The pituitary gland is an important endocrine organ that regulates estrus and reproduction in sheep mainly through hormone synthesis and secretion. Previous studies on reproduction traits have focused mainly on folliculogenesis and ovulation in sheep with different fecundities, and few systematic analyses of the mRNAs expressed in the pituitary have been performed. To explore the intrinsic molecular regulatory mechanisms and gene regulatory network of sheep reproductive traits, key genes affecting multiple fetal traits, such as ovulation number and litter size, were screened to provide a new reference for the study of reproduction traits in sheep.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eIn this study, three healthy small-tailed Han sheep and three healthy Wadi sheep were selected to form a high-reproduction group (small-tailed Han sheep, HP group) and a low-reproduction group (Wadi sheep, LP group). ONT full-length transcriptome sequencing technology was used for mRNA identification, screening, and functional analysis. A total of 7,123 DEGs were found between the two groups of sheep, including 3,551 genes that were upregulated and 3,572 genes that were downregulated in the HP group. The expression of screened genes \u003cem\u003ePRKACB\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eCAMK2D\u003c/em\u003e, \u003cem\u003ePIK3CB\u003c/em\u003e, \u003cem\u003eGNAI3\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003eMAPK10\u003c/em\u003e, and \u003cem\u003eMAPK13\u003c/em\u003e significantly differed between the HP and LP groups. GO and KEGG terms related to pituitary function and reproduction were enriched, including reproductive processes, responses to stimuli, and synapses. The related pathways included the mTOR signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, ERK1/2 signaling pathways and MAPK signaling pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results clearly indicate that the DEGs detected were involved in the structure development of tissues and organs, as well as the secretion of hormones in the endocrine system, which could provide a scientific basis for elucidating the genetic mechanisms of high reproduction in sheep.\u003c/p\u003e","manuscriptTitle":"Scanning and Mining of High Fecundity Genes by Oxford Nanopore Technologies (ONT) in Sheep (Ovis aries) Pituitary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:47:08","doi":"10.21203/rs.3.rs-4812389/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-01T09:58:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-31T05:05:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-31T05:02:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-07-27T09:45:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd8314a4-35bf-4e31-aa2f-d895de09bc61","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:01:02+00:00","versionOfRecord":{"articleIdentity":"rs-4812389","link":"https://doi.org/10.1186/s12864-025-11732-5","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-06-05 15:57:20","publishedOnDateReadable":"June 5th, 2025"},"versionCreatedAt":"2024-08-27 11:47:08","video":"","vorDoi":"10.1186/s12864-025-11732-5","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11732-5","workflowStages":[]},"version":"v1","identity":"rs-4812389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4812389","identity":"rs-4812389","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}